Name
Abstract | ||
|---|---|---|
The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. It is indicated by the membership values belonging to two fuzzy sets (i.e., the stronger rule set and the weaker rule set), and thus provides much more flexibility than traditional methods to discover some potentially more interesting association rules. Furthermore, revised algorithms based on Apriori algorithm and matrix structure are designed under this framework. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00500-010-0579-x | Soft Comput. |
Keywords | DocType | Volume |
traditional method,fuzzy interest measure,fuzzy set,traditional support,fuzzy method,stronger rule set,minimum support,association rule,minimum confidence,weaker rule set,interesting association rule,data mining,satisfiability | Journal | 15 |
Issue | ISSN | Citations |
6 | 1433-7479 | 3 |
PageRank | References | Authors |
0.43 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weimin Ma | 1 | 427 | 26.76 |
| Ke Wang | 2 | 61 | 9.24 |
| Zhu-Ping Liu | 3 | 3 | 0.77 |